Article published In: Human-centeredness in Translation: Advancing Translation Studies in a human-centered AI era
Guest-edited by Miguel A. Jiménez-Crespo
[InContext 5:1] 2025
► pp. 87–115
Risk in AI-mediated medical translation
On the essential role of professionals in providing adequate lexical solutions
Available under the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) 4.0 license.
For any use beyond this license, please contact the publisher at rights@benjamins.nl.
Published online: 31 May 2025
https://doi.org/10.54754/incontext.v5i1.108
https://doi.org/10.54754/incontext.v5i1.108
Abstract
Concerns about the future of AI implementation, particularly with the explosion of generative AI practices, are the result of the high impact AI is having in all areas of society and the need for debate and reflection about the role of technology in human practices. This paper addresses the medical translation field and the risks associated with the use and integration of AI technologies. To do so, it takes an interdisciplinary perspective that includes the human-centered AI (HCAI) paradigm in translation studies (e.g., Jiménez- Crespo, 2023; O’Brien, 2023), responsible AI (Arrieta et al., 2020), and AI for Social Good (Hager et al., 2019). More specifically, it reflects on areas where human agency is key at the lexical level in AI-mediated translation processes. In order to achieve this purpose, this paper reviews the notion of risk from the perspective of the translation of medical texts and their users, with emphasis in the multimodal forms of communication, which are in continuous growth and are often at the center of non-supervised automatic machine translated practices. This is illustrated with examples from a corpus analysis of human and AI solutions of English-Spanish translations of multimodal texts on mental health, an extremely sensitive and high-stakes domain where existing biases and stigma demand special attention. The focus of the analysis is on aspects such as the comparison of professional and AI translators’ solutions when dealing with terminological variation, interference, metaphor, cultural adequacy or multidimensionality. These key areas illustrate the importance of the human role in rendering appropriate solutions for different users’ profiles, including the role of creativity, an introspective human-specific skill, in promoting critical thinking and avoiding the bias and stigma of information on mental illness. Ultimately, the results highlight the need for a closer collaboration between technology and the humanities. This collaboration is needed to guarantee ethical practices in AI as well as to develop AI literacy in Translation Studies, and it should include the analysis of high-stakes areas in specific domains and the detection of risk and ways to tackle it.
Resumen
Con la explosión de la IA generativa, el futuro de la implementación de la tecnología ha despertado suspicacias, como resultado del alto impacto de la IA en todas las áreas de la vida y de la llamada a la necesidad de reflexión y debate sobre el papel de la tecnología en las actividades humanas. Este artículo se centra en los riesgos asociados al uso e integración de la IA en la traducción médica, desde una perspectiva interdisciplinar que incluye el paradigma de la IA centrada en las personas (HCAI) en los Estudios de Traducción (e.g., Jiménez-Crespo, 2023; O’Brien, 2023), la IA responsable (Arrieta et al., 2020), y la IA para el bien común (Hager et al., 2019). En concreto, en este artículo reflexionamos sobre ámbitos en los que el papel humano es clave a nivel léxico en la traducción mediada por la IA. Para ello, se revisa la noción de riesgo desde la perspectiva de la traducción de textos médicos y sus usuarios, con hincapié en las formas de comunicación multimodal, que están en continuo crecimiento y donde a menudo tienen lugar procesos de traducción automática sin supervisión humana. Para ilustrar esto, se toman ejemplos de un análisis de corpus de soluciones de traducción inglés-español de traductores profesionales y de la IA, para segmentos de textos multimodales sobre salud mental, un ámbito sensible y de alto riesgo donde los sesgos y el estigma existente exigen una atención especial. Nuestro análisis se centra en aspectos como la variación terminológica, la interferencia, la metáfora, la adecuación cultural y la multidimensionalidad, elementos que ponen de relieve la importancia del esencial papel humano de cara a ofrecer soluciones apropiadas a distintos usuarios. En este sentido, se destaca el papel de la creatividad, una cualidad humana introspectiva, en la promoción del pensamiento crítico y la eliminación de los sesgos y el estigma que pueden estar presentes en la información sobre salud mental. La colaboración entre la tecnología y las humanidades es fundamental para el desarrollo de una IA ética y responsable, así como para implementar programas de alfabetización en IA dentro de los Estudios de Traducción. Estos deben incluir procedimientos de análisis de áreas de alto riesgo en ámbitos específicos y la detección riesgo y modos de hacerle frente.
Palabras clave: Traducción mediada por IA, salud mental, riesgo, variación terminológica, creatividad
References (59)
Alarcón-Navío, Esperanza, Clara Inés López-Rodríguez and Maribel Tercedor-Sánchez. (2016). Variation dénominative et familiarité en tant que source d’incertitude en traduction médicale [Denominative variation and familiarity as a source of uncertainty in medical translation]. Meta, 61(1), 117–144.
Alkhalil, Munzer, Aula Abbara, Caroline Grangier and Abdulkarim Ekzayez. (2024). AI in conflict zones: The potential to revitalise healthcare in Syria and beyond. BMJ Global Health, 91, e015755.
American Psychiatric Association. (2020). Bias and stigma in healthcare systems. Retrieved September 1, 2024 from [URL]
Arija-Val, Victoria, María José Santi-Cano, José Pedro Novalbos-Ruiz, Josefa Canals and Amelia Rodríguez-Martín. (2022). Caracterización, epidemiología y tendencias de los trastornos de la conducta alimentaria [Characterization, epidemiology, and trends of eating disorders]. Nutrición hospitalaria, 39(SPE2), 8–15.
Arrieta, Alejandro Barredo, Natalia Díaz-Rodríguez, Javier Del Ser, Adrien Bennetot, Siham Tabik, Alberto Barbado, Salvador García, Sergio Gil-López, Daniel Molina and Richard Benjamins. (2020). Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 581, 82–115.
Bayer-Hohenwarter, Gerrit. (2009). Translational creativity: How to measure the unmeasurable. In Susanne Göpferich, Arnt Lykke Jakobsen & Inger M. Mees (Eds.), Behind the Mind: Methods, Models and Results in Translation Process Research (pp. 39–59). Samfundslitteratur.
Bowker, Lynne. (2023). De-mystifying Translation: Introducing Translation to Non-translators. Routledge.
. (2021). Teaching resources. Machine Translation Literacy. Retrieved August 20, 2024 from [URL]
. (1997). Multidimensional classification of concepts and terms. In Sue Ellen Wright & Gerhard Budin (Eds.), Handbook of Terminology Management (Vol. 11, pp. 133–143). John Benjamins.
Canfora, Carmen and Angelika Ottmann. (2020). Risks in neural machine translation. Translation Spaces, 9(1), 58–77.
Chesney, Edward, Guy M. Goodwin and Seena Fazel. (2014). Risks of all-cause and suicide mortality in mental disorders: A meta-review. World Psychiatry, 13(2), 153–160.
Costa-jussà, Marta R., James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield, Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang, Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip Hansanti, John Hoffman, … NLLB Team. (2024). Scaling neural machine translation to 200 languages. Nature, 630(8018), 841–846.
Demonte, Violeta. (2024, February 20). Entre la exaltación y el miedo: ¿la IA nos quitará la palabra? [Between exaltation and fear: Will AI take away our voice?]. Revista de Libros. [URL]
European Parliament. (2024). EU AI Act: First regulation on artificial intelligence. Retrieved September 1, 2024 from [URL]
Federici, Federico M., Christophe Declercq, Jorge Díaz Cintas and Rocío Baños Piñero. (2023). Ethics, automated processes, machine translation, and crises. In Helena Moniz & Carla Parra Escartín (Eds.), Towards Responsible Machine Translation: Ethical and Legal Considerations in Machine Translation (pp. 135–156). Springer.
Fernández-Silva, Sabela, Judit Freixa and M. Teresa Cabré Castellví. (2011). A proposed method for analysing the dynamics of cognition through term variation. Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication, 17(1), 49–74.
Fernández Silva, Sabela and Maribel Tercedor-Sánchez. (2019). El desarrollo de la competencia sobre variación terminológica en estudiantes de traducción e interpretación: una experiencia de colaboración interuniversitaria [The development of terminological variation competence in translation and interpreting students: An interuniversity collaboration experience]. RLA. Revista de lingüística teórica y aplicada, 57(2), 137–163.
Gallagher, Kieran A., Kendrin R. Sonneville, Vivienne M. Hazzard, Traci L. Carson and Belinda L. Needham. (2021). Evaluating gender bias in an eating disorder risk assessment questionnaire for athletes. Eating Disorders, 29(1), 29–41.
Gernsbacher, Morton Ann. (2015). Video captions benefit everyone. Policy Insights from the Behavioral and Brain Sciences, 2(1), 195–202.
Grilli, Luca and Mattia Pedota. (2024). Creativity and artificial intelligence: A multilevel perspective. Creativity and Innovation Management, 33(2), 234–247.
Guerberof-Arenas, Ana and Antonio Toral. (2023). To be or not to be: A translation reception study of a literary text translated into Dutch and Catalan using machine translation. arXiv e-prints, 2307.02358.
Hager, Gregory D., Ann Drobnis, Fei Fang, Rayid Ghani, Amy Greenwald, Terah Lyons, David C. Parkes, Jason Schultz, Suchi Saria and Stephen F. Smith. (2019). Artificial intelligence for social good. arXiv preprint, 1901.05406.
Herrero-Diz, Paula, Marta Pérez-Escolar and Juan Francisco Plaza Sánchez. (2020). Desinformación de género: análisis de los bulos de Maldito Feminismo [Gender disinformation: Analysis of the hoaxes by Maldito Feminismo]. Icono14, 18(2), 188–216.
Jiménez-Crespo, Miguel Ángel. (2023). Augmentation and translation crowdsourcing: Are collaborative translators’ minds truly “augmented”? Translation, Cognition & Behavior, 7(2), 291–310.
Jiménez-Crespo, Miguel Ángel and Maribel Tercedor-Sánchez. (2021). Explicitation and implicitation in translation: Combining comparable and parallel corpus methodologies. MonTI. Monografías de Traducción e Interpretación, 131, 62–92.
. (2017). Lexical variation, register and explicitation in medical translation: A comparable corpus study of medical terminology in US websites translated into Spanish. Translation and Interpreting Studies, 12(3), 405–426.
Kenny, Dorothy and Marion Winters. (2020). Machine translation, ethics and the literary translator’s voice. Translation Spaces, 9(1), 123–149.
Khoong, Elaine C., Eric Steinbrook, Cortlyn Brown and Alicia Fernandez. (2019). Assessing the use of Google Translate for Spanish and Chinese translations of emergency department discharge instructions. JAMA Internal Medicine, 179(4), 580–582.
Krüger, Ralph. (2020). Explicitation in neural machine translation. Across Languages and Cultures, 21(2), 195–216.
Lakoff, George. (1987). Women, Fire, and Dangerous Things: What Categories Reveal about the Mind. University of Chicago Press.
León-Araúz, Pilar, Arianne Reimerink and Melania Cabezas-García. (2020). Representing multiword term variation in a terminological knowledge base: A corpus-based study. Proceedings of the Twelfth Language Resources and Evaluation Conference, Marseille, France.
Lopez-Medel, Maria. (2021). Gender bias in machine translation: An analysis of Google Translate in English and Spanish. Academia Letters, Article 2288.
López-Rodríguez, Clara Inés and Maribel Tercedor-Sánchez. (2025). Emotions in multimodal texts: Challenges for accessible translation. Translation and Translanguaging in Multilingual Contexts, 11(2), 149–170.
. (2017). Identification and understanding of medical metaphors by non-experts. In Francesca Ervas, Elisabetta Gola & Maria Grazia Rossi (Eds.), Metaphor in Communication, Science and Education (pp. 217–246). De Gruyter Mouton.
Lupton, Deborah. (2021). Young people’s use of digital health technologies in the Global North: Narrative review [Review]. Journal of Medical Internet Research, 23(1), e18286.
Mihalache, Iulia. (2024, July 2–4). Ethical analysis of AI-enhanced audiovisual translation tools. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Osaka, Japan.
Nurminen, Mary and Niko Papula. (2018). Gist MT users: A snapshot of the use and users of one online MT tool. Proceedings of the 21st Annual Conference of the European Association for Machine Translation, Alicante, Spain.
O’Brien, Sharon. (2023). Human-centered augmented translation: Against antagonistic dualisms. Perspectives, 32(3), 391–406.
Pym, Anthony. (2008). On Toury’s laws of how translators translate. In Anthony Pym, Miriam Shlesinger & Daniel Simeoni (Eds.), Beyond Descriptive Translation Studies: Investigations in Homage to Gideon Toury (pp. 311–328). John Benjamins.
Risku, Hanna. (2010). A cognitive scientific view on technical communication and translation: Do embodiment and situatedness really make a difference? Target. International Journal of Translation Studies, 22(1), 94–111.
Rogers, Yvonne. (2022). Commentary: Human-centred AI: The new zeitgeist. Human–computer Interaction, 37(3), 254–255.
Rojo, Ana and Purificación Meseguer. (2015). Fomentando la creatividad: una propuesta didáctica para el aula de traducción [Fostering creativity: A didactical proposal for the translation class]. Quaderns. Revista de Traducció, 221, 255–271.
Rössler, Wulf. (2016). The stigma of mental disorders: A millennia-long history of social exclusion and prejudices. EMBO Reports, 17(9), 1250–1253.
Save the Children. (2024). Desinformación y discursos de odio en el entorno digital [Disinformation and hate speech in the digital environment]. [URL]
Savoldi, Beatrice, Alan Ramponi, Matteo Negri and Luisa Bentivogli. (2025). Translation in the hands of many: Centering lay users in machine translation interactions. arXiv preprint, 2502.13780.
Semino, Elena, Zsófia Demjén and Jane Demmen. (2018). An integrated approach to metaphor and framing in cognition, discourse, and practice, with an application to metaphors for cancer. Applied Linguistics, 39(5), 625–645.
Steen, Gerard. (2007). Finding metaphor in discourse: Pragglejaz and beyond. Cultura, lenguaje y representación, 51, 9–25.
Tercedor-Sánchez, Maribel. (2011). The cognitive dynamics of terminological variation. Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication, 17(2), 181–197.
Tercedor-Sánchez, Maribel and Alicia Casado Valenzuela. (2018). Visual metaphors in medical knowledge representation. Linguistica Antverpiensia, New Series — Themes in Translation Studies, 171, 174–195.
Tercedor-Sánchez, Maribel and Clara I. López-Rodríguez. (2012). Access to health in an intercultural setting: The role of corpora and images in grasping term variation. Linguistica Antverpiensia, New Series — Themes in Translation Studies, 111, 247–268.
Tercedor-Sánchez, Maribel and Antonio Jesús Láinez Ramos-Bossini. (2020). Resemblance metaphors and embodiment as iconic markers in medical understanding and communication by non-experts. In Pamela Perniss, Olga Fischer & Christina Ljungberg (Eds.), Operationalizing Iconicity (pp. 265–290). John Benjamins Publishing Company.
Toral, Antonio. (2019). Post-editese: An exacerbated translationese. Proceedings of Machine Translation Summit XVII: Research Track, Dublin, Ireland.
Tuominen, Tiina, Catalina Jiménez Hurtado and Anne Ketola. (2018). Why methods matter: Approaching multimodality in translation research. Linguistica Antverpiensia: New Series — Themes in Translation Studies, 171, 1–21.
Vega-Expósito, Miguel. (2024). Análisis cuantitativo de traducciones del inglés al español poseditadas por traductores institucionales [Quantitative analysis of English-to-Spanish translations post-edited by institutional translators]. Entreculturas: revista de traducción y comunicación intercultural, 141, 80–99.
Vieira, Lucas Nunes, Minako O’Hagan and Carol O’Sullivan. (2021). Understanding the societal impacts of machine translation: A critical review of the literature on medical and legal use cases. Information, Communication & Society, 24(11), 1515–1532.